Identifying vital nodes algorithm in social networks fusing higher-order information

Guanghui YAN; Meng ZHANG; Hao LUO; Shikui LI; Ting LIU

doi:10.11959/j.issn.1000-436x.2019198

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Identifying vital nodes algorithm in social networks fusing higher-order information

Papers | 更新时间：2024-06-05

- Identifying vital nodes algorithm in social networks fusing higher-order information
- Journal on Communications Vol. 40, Issue 10, Pages: 109-118(2019)
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730070
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61662066);The National Natural Science Foundation of China(61163010);The Natural Science Foundation for Young Scientists of Gansu Province(1606RJYA222)
- DOI：10.11959/j.issn.1000-436x.2019198
  CLC： TP181，TP391
- Online First：2019-10，
  
  Published：25 October 2019
- 稿件说明：
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Guanghui YAN, Meng ZHANG, Hao LUO, et al. Identifying vital nodes algorithm in social networks fusing higher-order information[J]. Journal on Communications, 2019, 40(10): 109-118.
DOI：

Guanghui YAN, Meng ZHANG, Hao LUO, et al. Identifying vital nodes algorithm in social networks fusing higher-order information[J]. Journal on Communications, 2019, 40(10): 109-118. DOI： 10.11959/j.issn.1000-436x.2019198.

摘要

识别重要节点是复杂网络研究的基础性问题。现有理论框架主要以“点-边”这种低阶结构为基本单元，往往忽略了多个节点之间可能存在的交互性、传递性等重要因素。为了更加精确地识别重要节点，对网络中以模体为基本单元的高阶结构进行了研究，首先，提出了节点高阶度的概念，进一步引入证据理论融合了节点的高阶结构和低阶结构信息，设计了一种融合节点高阶信息的半局部重要节点识别方法。在3个真实社交网络上的实验结果表明，相较于只关注低阶结构的已有方法，所提出的算法能够更加精确地识别网络中的重要节点。

Abstract

Identifying vital nodes is a basic problem in complex network research.The existing theoretical framework

mainly considered from the lower-order structure of node-based and edge-based relations often ignores important factors such as interactivity and transitivity between multiple nodes.To identify vital nodes more accurately

the motif

the high-er-order structure of the network

was studied as the basic unit.Firstly

a notion of higher-order degree of nodes in a com-plex network was proposed.Then

the higher-order structure and lower-order structure of nodes were fused into evidence theory.A semi-local identifying vital nodes algorithm fusing higher-order information of nodes was designed.The results of experiments on three real social networks show that the proposed algorithm can identify vital nodes more accurately in the network than the existing methods which only focus on the low-order structure.

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张静 , 唐杰 . 社会影响力分析综述 [J ] . 中国科学:信息科学 , 2017 , 47 ( 8 ): 967 - 979 .

ZHANG J , TANG J . Survey of social influence analysis and modeling [J ] . SCIENTIA SINICA Informationis , 2017 , 47 ( 8 ): 967 - 979 .

韩忠明 , 陈炎 , 刘雯 , 等 . 社会网络节点影响力分析研究 [J ] . 软件学报 , 2017 , 28 ( 1 ): 84 - 104 .

HAN Z M , CHEN Y , LIU W , et al . Research on node influence analysis in social networks [J ] . Journal of Software , 2017 , 28 ( 1 ): 84 - 104 .

BONACICH P F . Factoring and weighting approaches to status scores and clique identification [J ] . Journal of Mathematical Sociology , 1972 , 2 ( 1 ): 113 - 120 .

任晓龙 , 吕琳媛 . 网络重要节点排序方法综述 [J ] . 科学通报 , 2014 , 59 ( 13 ): 1175 - 1197 .

REN X L , LYU L Y . Review of ranking nodes in complex networks [J ] . Chinese Science Bulletin , 2014 , 59 ( 13 ): 1175 - 1197 .

WEI D , DENG X , ZHANG X , et al . Identifying influential nodes in weighted networks based on evidence theory [J ] . Physica A:Statistical Mechanics and its Applications , 2013 , 392 ( 10 ): 2564 - 2575 .

MICHAEL J M . Applied network analysis:a method logical introduc tion [M ] . New York : Sage PublicationsPress , 1983 .

FREEMAN L C . A set of measures of centrality based on betweenness [J ] . Sociometry , 1977 , 40 ( 1 ): 35 - 41 .

FREEMAN L C . Centrality in social networks conceptual clarification [J ] . Social Networks , 1978 , 1 ( 3 ): 215 - 239 .

BRIN S , PAGE L . The anatomy of a large-scale hypertextual Web search engine [J ] . Computer Networks and ISDN Systems , 1998 : 107 - 117 .

LYU L Y , ZHANG Y C , YEUNG C H , et al . Leaders in social networks,the delicious case [J ] . PLOS ONE , 2011 , 6 ( 6 ): 1 - 9 .

ALON U . Network motifs:theory and experimental approaches [J ] . Nature Reviews Genetics , 2007 , 8 ( 6 ): 450 - 461 .

PRŽULJ N , CORNEIL D G , JURISICA I . Modeling interactome:scale-free or geometric? [J ] . Bioinformatics , 2004 , 20 ( 18 ): 3508 - 3515 .

MILO R , SHEN-ORR S , ITZKOVITZ S , et al . Network motifs:simple building blocks of complex networks [J ] . Science , 2002 , 298 ( 5594 ): 824 - 827 .

SHEN-ORR S , MILO R , MANGAN S , et al . Network motifs in the transcriptional regulation network of escherichia coli [J ] . Nature Genetics , 2002 , 31 ( 1 ): 64 - 68 .

BENSON A R , GLEICH D F , LESKOVEC J . Higher-order organization of complex networks [J ] . Science , 2016 , 353 ( 6295 ): 163 - 166 .

YIN H , BENSON A R , LESKOVEC J , et al . Local higher-order graph clustering [C ] // The 23rd ACM SIGKDD International Conference . 2017 : 555 - 564 .

BENSON A R . Tools for higher-order network analysis [D ] . Palo Alto:Stanford University , 2018 .

DAVIS J , LEINHARDT S . The structure of positive interpersonal relations in small groups [J ] . J Berger Sociological Theories in Progress , 1967 :54.

WASSERMAN S . Social network analysis methods and applications [J ] . Contemporary Sociology , 1995 , 91 ( 435 ): 219 - 220 .

SCHANK T , WAGNER D . Finding,counting and listing all triangles in large graphs,an experimental study [M ] . Heidelberg : Springer Berlin HeidelbergPress , 2005 .

蒋雯 , 邓鑫洋 . D-S证据理论信息建模与应用 [M ] . 北京 : 科学出版社 , 2018 .

JIANG W , DENG X Y . Information modeling and application of D-S evidence theory [M ] . Beijing : Science PressPress , 2018 .

SHAFER G . A mathematical theory of evidence by glenn shafer [J ] . Journal of the American Statistical Association , 1978 , 73 ( 363 ): 677 - 678 .

CHEN D B , LYU L Y , SHANG M S , et al . Identifying influential nodes in complex networks [J ] . Physica A:Statistical Mechanics and its Applications , 2012 , 391 ( 4 ): 1777 - 1787 .

GAO C , WEI D J , HU Y , et al . A modified evidential methodology of identifying influential nodes in weighted networks [J ] . Physica A:Statistical Mechanics and its Applications , 2013 , 392 ( 21 ): 5490 - 5500 .

周涛 , 傅忠谦 , 牛永伟 , 等 . 复杂网络上传播动力学研究综述 [J ] . 自然科学进展 , 2005 , 15 ( 5 ): 513 - 518 .

ZHOU T , FU Z Q , NIU Y W , et al . A survey of propagation dynamics in complex networks [J ] . Progress in Natural Science , 2005 , 15 ( 5 ): 513 - 518 .

王庆 . 自我中心网络的结构建模与研究 [D ] . 北京:北京邮电大学 , 2017 .

WANG Q . On ego network modeling and analysis [D ] . Beijing:Beijing University of Posts and Telecommunications , 2017 .

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